Debris disks are cold dust belts hosted by some main-sequence stars, composed of micrometer-size grains to kilometer-size planetesimals. As left-overs from planet formation, the study of these young cousins of our own solar system’s Kuiper belt can help us to better understand how planets are formed.

To do so, we need to image these disks in the visible or near-infrared, to deduce their composition and physical properties from the starlight scattered by the dust, and maybe also detect signposts of planets in their geometry. However, despite numerous surveys with the Hubble Space Telescope (HST) and the largest ground-based telescopes, only 19 debris disks had been imaged in scattered light so far. As far as planets are concerned, only 24 have been directly imaged, in significant contrast with the large numbers of discoveries by the radial velocity and the transit methods (with hundreds and thousands of planet detections respectively).

This is due to the very high contrast between the host star and the light reflected by a disk or emitted by a planet (more than a million times fainter!). To achieve such challenging detections, the instruments need to be equipped with carefully optimized coronagraphs, and with efficient adaptive optics systems for ground-based telescopes, and furthermore, the observer has to apply post-processing techniques on the resulting images to detect the dim circumstellar material.

The classical post-processing method consists of subtracting the image of a reference star from the science image to reveal material in its vicinity. However, such a subtraction is never perfect due to telescope instabilities and/or residual wavefront errors, and residual starlight still impedes the detection of cold material within 2’’ of the star (Fig. 1). New algorithms have been recently developed to solve this issue, by using large libraries of reference star images to generate a synthetic image of the star optimized to the actual science image. These new techniques improve the starlight subtraction by a factor of 10 to 100 over the classical method (Fig. 2).

Figure 1: The Principle of the classical post-processing technique: the image of a reference star is subtracted from the science image to remove the starlight. Although this method improves the contrast by a factor of 5 to 10 compared to the raw image, the telescope instabilities prevent the detection of any material within 2’’ from the star.

Figure 2: Images of the debris disk around HD181327 reduced with the classical technique (left, from [1]) and with the KLIP algorithm [2] (right, from [3]). This advanced post-processing algorithm typically improves the contrast by a factor of 10 to 100 over the classical method.

Our team has thus started the project of reprocessing the entire HST-NICMOS coronagraphic archive with such advanced algorithms to reveal new disks and planet candidates [4]. The archive is composed of images of 400 stars observed in the near-infrared between 1997 and 2008 and have been underexploited by the use of mainly old post-processing techniques. Among our recent discoveries from this project is the detection of five debris disks seen for the first time in scattered light (Fig. 3). These detections increase the total number by more than 20%. The on-going analysis and modeling of these disks should tell us more about their composition and properties and maybe present hints of possible planets.

This Month’s Featured Author

Dr. Brian Williams received his B.S. from Florida State University in 2004 and his Ph.D. from North Carolina State University in 2010. He was a NASA Postdoctoral Fellow at NASA Goddard Space Flight Center for three years, after which he worked as a research scientist at NASA GSFC with Universities Space Research Association. He arrived at STScI in February of 2017, and is currently a Support Scientist in the Science Mission Office. His research interests include supernovae and supernova remnants, shock physics and particle acceleration, and dust in the interstellar medium.